{
 "cells": [
  {
   "cell_type": "markdown",
   "source": [
    "## ResNet——CIFAR-10"
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "d1a72adc537c8424"
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "initial_id",
   "metadata": {
    "collapsed": true,
    "ExecuteTime": {
     "end_time": "2025-06-11T00:43:54.714180Z",
     "start_time": "2025-06-11T00:43:48.549301Z"
    }
   },
   "outputs": [],
   "source": [
    "import os\n",
    "import pickle\n",
    "import numpy as np\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.optim as optim\n",
    "import torchvision.transforms as transforms\n",
    "from torch.utils.data import Dataset, DataLoader\n",
    "from sklearn.metrics import accuracy_score, precision_recall_fscore_support, confusion_matrix, classification_report\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import time"
   ]
  },
  {
   "cell_type": "code",
   "outputs": [],
   "source": [
    "# 数据集路径\n",
    "cifar10_data_dir = r'D:\\Machine_learning\\jiqixuexi\\cifar-10-python\\cifar-10-batches-py'\n",
    "\n",
    "# 自定义CIFAR-10数据集加载函数\n",
    "def load_cifar10(data_dir):\n",
    "    def unpickle(file):\n",
    "        with open(file, 'rb') as fo:\n",
    "            dict = pickle.load(fo, encoding='bytes')\n",
    "        return dict\n",
    "\n",
    "    # 加载训练数据\n",
    "    train_data = []\n",
    "    train_labels = []\n",
    "    for i in range(1, 6):\n",
    "        batch_file = os.path.join(data_dir, f'data_batch_{i}')\n",
    "        batch = unpickle(batch_file)\n",
    "        train_data.append(batch[b'data'])\n",
    "        train_labels.extend(batch[b'labels'])\n",
    "    train_data = np.array(train_data).reshape(-1, 3, 32, 32)\n",
    "    train_labels = np.array(train_labels)\n",
    "\n",
    "    # 加载测试数据\n",
    "    test_file = os.path.join(data_dir, 'test_batch')\n",
    "    test_batch = unpickle(test_file)\n",
    "    test_data = np.array(test_batch[b'data']).reshape(-1, 3, 32, 32)\n",
    "    test_labels = np.array(test_batch[b'labels'])\n",
    "\n",
    "    # 归一化数据\n",
    "    train_data = train_data / 255.0\n",
    "    test_data = test_data / 255.0\n",
    "\n",
    "    return train_data, test_data, train_labels, test_labels\n",
    "\n",
    "# 自定义CIFAR-10数据集类\n",
    "class CustomCIFAR10Dataset(Dataset):\n",
    "    classes = ['plane', 'car', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck']  # 添加类别信息\n",
    "\n",
    "    def __init__(self, images, labels):\n",
    "        self.images = images\n",
    "        self.labels = labels\n",
    "\n",
    "    def __len__(self):\n",
    "        return len(self.images)\n",
    "\n",
    "    def __getitem__(self, idx):\n",
    "        image = self.images[idx]\n",
    "        label = self.labels[idx]\n",
    "        # 将图像转换为PyTorch张量\n",
    "        image = torch.from_numpy(image).float()\n",
    "        label = torch.from_numpy(np.array(label)).long() \n",
    "        return image, label\n",
    "\n",
    "# 加载数据集\n",
    "train_data, test_data, train_labels, test_labels = load_cifar10(cifar10_data_dir)\n",
    "\n",
    "# 定义数据预处理\n",
    "transform = transforms.Compose([\n",
    "    transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010))\n",
    "])\n",
    "\n",
    "# 创建自定义数据集\n",
    "train_dataset = CustomCIFAR10Dataset(train_data, train_labels)\n",
    "test_dataset = CustomCIFAR10Dataset(test_data, test_labels)\n",
    "\n",
    "# 创建数据加载器\n",
    "train_loader = DataLoader(train_dataset, batch_size=64, shuffle=True)\n",
    "test_loader = DataLoader(test_dataset, batch_size=64, shuffle=False)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2025-06-11T01:02:00.287080Z",
     "start_time": "2025-06-11T01:01:59.663148Z"
    }
   },
   "id": "d26803ae02897585",
   "execution_count": 10
  },
  {
   "cell_type": "code",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch [1/10], Loss: 1.5485\n",
      "Epoch [2/10], Loss: 1.1138\n",
      "Epoch [3/10], Loss: 0.9080\n",
      "Epoch [4/10], Loss: 0.7710\n",
      "Epoch [5/10], Loss: 0.6639\n",
      "Epoch [6/10], Loss: 0.5670\n",
      "Epoch [7/10], Loss: 0.4887\n",
      "Epoch [8/10], Loss: 0.4100\n",
      "Epoch [9/10], Loss: 0.3512\n",
      "Epoch [10/10], Loss: 0.2912\n",
      "Training Time: 65.33 seconds\n"
     ]
    }
   ],
   "source": [
    "# 定义适用于CIFAR-10的ResNet模型\n",
    "class ResNet(nn.Module):\n",
    "    def __init__(self):\n",
    "        super(ResNet, self).__init__()\n",
    "        self.conv1 = nn.Conv2d(3, 32, 3, padding=1)\n",
    "        self.conv2 = nn.Conv2d(32, 64, 3, padding=1)\n",
    "        self.conv3 = nn.Conv2d(64, 128, 3, padding=1)\n",
    "        self.conv4 = nn.Conv2d(128, 256, 3, padding=1)\n",
    "        self.pool = nn.MaxPool2d(2, 2)\n",
    "        self.dropout = nn.Dropout(0.5)\n",
    "        self.fc1 = nn.Linear(256 * 4 * 4, 512)\n",
    "        self.fc2 = nn.Linear(512, 10)\n",
    "\n",
    "    def forward(self, x):\n",
    "        x = self.pool(torch.relu(self.conv1(x)))\n",
    "        x = self.pool(torch.relu(self.conv2(x)))\n",
    "        x = self.pool(torch.relu(self.conv3(x)))\n",
    "        x = torch.relu(self.conv4(x))\n",
    "        x = x.view(-1, 256 * 4 * 4)\n",
    "        x = torch.relu(self.fc1(x))\n",
    "        x = self.dropout(x)\n",
    "        x = self.fc2(x)\n",
    "        return x\n",
    "\n",
    "# 初始化模型、损失函数和优化器\n",
    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
    "model = ResNet().to(device)\n",
    "criterion = nn.CrossEntropyLoss()\n",
    "optimizer = optim.Adam(model.parameters(), lr=0.001)\n",
    "\n",
    "# 记录训练时间\n",
    "start_time = time.time()\n",
    "\n",
    "# 训练模型\n",
    "num_epochs = 10\n",
    "for epoch in range(num_epochs):\n",
    "    model.train()\n",
    "    epoch_loss = 0.0\n",
    "    for i, (images, labels) in enumerate(train_loader):\n",
    "        images = images.to(device)\n",
    "        labels = labels.to(device)\n",
    "        optimizer.zero_grad()\n",
    "        outputs = model(images)\n",
    "        loss = criterion(outputs, labels)\n",
    "        epoch_loss += loss.item()\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "    print(f'Epoch [{epoch+1}/{num_epochs}], Loss: {epoch_loss / len(train_loader):.4f}')\n",
    "\n",
    "train_time = time.time() - start_time\n",
    "print(f\"Training Time: {train_time:.2f} seconds\")"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2025-06-11T01:03:06.799389Z",
     "start_time": "2025-06-11T01:02:01.437733Z"
    }
   },
   "id": "e00483168a44e782",
   "execution_count": 11
  },
  {
   "cell_type": "code",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Accuracy: 0.7640\n",
      "Inference Time: 1.55 seconds\n"
     ]
    }
   ],
   "source": [
    "# 测试模型\n",
    "model.eval()\n",
    "start_inference = time.time()\n",
    "\n",
    "with torch.no_grad():\n",
    "    correct = 0\n",
    "    total = 0\n",
    "    all_labels = []\n",
    "    all_preds = []\n",
    "    for images, labels in test_loader:\n",
    "        images = images.to(device)\n",
    "        labels = labels.to(device)\n",
    "        outputs = model(images)\n",
    "        _, predicted = torch.max(outputs.data, 1)\n",
    "        total += labels.size(0)\n",
    "        correct += (predicted == labels).sum().item()\n",
    "        all_labels.extend(labels.cpu().numpy())\n",
    "        all_preds.extend(predicted.cpu().numpy())\n",
    "    test_acc = correct / total\n",
    "    print(f\"Test Accuracy: {test_acc:.4f}\")\n",
    "\n",
    "inference_time = time.time() - start_inference\n",
    "print(f\"Inference Time: {inference_time:.2f} seconds\")"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2025-06-11T01:03:17.675078Z",
     "start_time": "2025-06-11T01:03:16.104288Z"
    }
   },
   "id": "77035ed135a1fed6",
   "execution_count": 12
  },
  {
   "cell_type": "code",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.7640\n",
      "Precision: 0.7640\n",
      "Recall: 0.7640\n",
      "F1-score: 0.7631\n",
      "Training Time: 65.33 seconds\n",
      "Inference Time: 1.55 seconds\n",
      "Confusion Matrix:\n",
      " [[778  12  64  16  12   3  10  12  57  36]\n",
      " [ 15 838   1   7   0   4   9   1  32  93]\n",
      " [ 56   7 662  47  94  43  48  21  11  11]\n",
      " [ 20  14  60 587  81 121  60  25  13  19]\n",
      " [ 20   3  49  51 770  15  33  50   7   2]\n",
      " [  7   3  52 151  60 641  23  48   9   6]\n",
      " [  3   3  21  47  42  20 845   5   9   5]\n",
      " [ 14   1  33  39  75  52   3 765   4  14]\n",
      " [ 40  23  14   9   3   2   3   2 885  19]\n",
      " [ 27  45   5   9   6   7   2   6  24 869]]\n",
      "Classification Report:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "       plane       0.79      0.78      0.79      1000\n",
      "         car       0.88      0.84      0.86      1000\n",
      "        bird       0.69      0.66      0.68      1000\n",
      "         cat       0.61      0.59      0.60      1000\n",
      "        deer       0.67      0.77      0.72      1000\n",
      "         dog       0.71      0.64      0.67      1000\n",
      "        frog       0.82      0.84      0.83      1000\n",
      "       horse       0.82      0.77      0.79      1000\n",
      "        ship       0.84      0.89      0.86      1000\n",
      "       truck       0.81      0.87      0.84      1000\n",
      "\n",
      "    accuracy                           0.76     10000\n",
      "   macro avg       0.76      0.76      0.76     10000\n",
      "weighted avg       0.76      0.76      0.76     10000\n"
     ]
    }
   ],
   "source": [
    "# 计算评估指标\n",
    "accuracy = accuracy_score(all_labels, all_preds)\n",
    "precision, recall, f1, _ = precision_recall_fscore_support(all_labels, all_preds, average='weighted')\n",
    "conf_matrix = confusion_matrix(all_labels, all_preds)\n",
    "class_report = classification_report(all_labels, all_preds, target_names=CustomCIFAR10Dataset.classes)\n",
    "\n",
    "# 输出结果\n",
    "\n",
    "print(f\"Accuracy: {accuracy:.4f}\")\n",
    "print(f\"Precision: {precision:.4f}\")\n",
    "print(f\"Recall: {recall:.4f}\")\n",
    "print(f\"F1-score: {f1:.4f}\")\n",
    "print(f\"Training Time: {train_time:.2f} seconds\")\n",
    "print(f\"Inference Time: {inference_time:.2f} seconds\")\n",
    "print(\"Confusion Matrix:\\n\", conf_matrix)\n",
    "print(\"Classification Report:\\n\", class_report)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2025-06-11T01:03:19.640164Z",
     "start_time": "2025-06-11T01:03:19.557284Z"
    }
   },
   "id": "82dd34538bede52a",
   "execution_count": 13
  },
  {
   "cell_type": "code",
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 1200x1000 with 2 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 绘制混淆矩阵热力图\n",
    "plt.figure(figsize=(12, 10))\n",
    "sns.heatmap(conf_matrix, annot=True, fmt='d', cmap='Blues')\n",
    "plt.title(\"ResNet - Confusion Matrix (CIFAR-10)\")\n",
    "plt.xlabel(\"Predicted Label\")\n",
    "plt.ylabel(\"True Label\")\n",
    "plt.show()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2025-06-11T01:03:21.907469Z",
     "start_time": "2025-06-11T01:03:20.972117Z"
    }
   },
   "id": "e2bfdf22ca7c19ca",
   "execution_count": 14
  },
  {
   "cell_type": "code",
   "outputs": [],
   "source": [],
   "metadata": {
    "collapsed": false
   },
   "id": "ff7e7acc2a28e8df"
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
